- The "Odds + Win Rate Strategy" was constructed by combining the risk budgets of the odds strategy and the win rate strategy, resulting in a comprehensive score. The strategy has achieved an annualized return of 6.8% since 2011, with a maximum drawdown of 2.9%. Since 2014, the annualized return was 7.4%, with a maximum drawdown of 2.3%. From 2019 onwards, the annualized return was 6.5%, with a maximum drawdown of 2.3%[3][46][48] - The "Small Cap Factor" is characterized by medium odds (0.1 standard deviation), strong trend (1.3 standard deviation), and low crowding (-1.1 standard deviation). Its comprehensive score has risen significantly to 3.2, indicating improved allocation value[19][20][21] - The "Value Factor" exhibits high odds (1.0 standard deviation), moderate trend (-0.2 standard deviation), and low crowding (-1.4 standard deviation). Its comprehensive score is 3, suggesting it is relatively favorable compared to other factors[21][23][34] - The "Quality Factor" currently shows high odds (1.2 standard deviation), moderate crowding (-0.2 standard deviation), but weak trend (-1.0 standard deviation). Its comprehensive score is -0.6, indicating lower allocation value[24][25][26] - The "Growth Factor" is in a high crowding state, with odds at 0.5 standard deviation, trend at 0.3 standard deviation, and crowding at 1.3 standard deviation. Its comprehensive score has dropped to -0.8, highlighting higher trading risks[27][28][29] - The "Odds-Enhanced Strategy" focuses on overweighting high-odds assets and underweighting low-odds assets under a target volatility constraint. Since 2011, it has achieved an annualized return of 6.7% with a maximum drawdown of 3.1%. From 2014, the annualized return was 7.5%, with a maximum drawdown of 2.8%. Since 2019, the annualized return was 7.0%, with a maximum drawdown of 2.8%[40][41][42] - The "Win Rate-Enhanced Strategy" derives macro win rate scores from five factors: currency, credit, growth, inflation, and overseas. Since 2011, it has achieved an annualized return of 7.2% with a maximum drawdown of 3.4%. From 2014, the annualized return was 8.1%, with a maximum drawdown of 2.2%. Since 2019, the annualized return was 7.0%, with a maximum drawdown of 1.5%[43][44][45]

Quantitative Models and Construction Methods 1. Model Name: Size Rotation Model Based on Odds and Win Rates - **Model Construction Idea**: The model integrates the concepts of odds and win rates to capture the rotation between large-cap and small-cap stocks. Odds are derived from valuation differences, while win rates are calculated using multiple indicators[4][30][40] - **Model Construction Process**: - **Odds Calculation**: - Define odds as the ratio of average positive returns to the absolute value of average negative returns - Formula: $ \mathbb{R}_{\mathbb{B}}^{\pm}\,\mathbb{R}=-\frac{\sum_{i=1}^{n}r e t u r n_{i}\,/n}{\sum_{j=1}^{m}r e t u r n_{j}\,/m} $ where $ \mathbb{R}_{\mathbb{B}}^{+} $ represents positive returns and $ \mathbb{R}_{\mathbb{B}}^{-} $ represents negative returns[30][31] - Use historical valuation differences between large-cap (CSI 300) and small-cap (CSI 2000) indices to estimate odds through linear regression[32][36] - **Win Rate Calculation**: - Combine multiple indicators (e.g., Shibor, short-term credit spread, market trend, market volatility, style momentum, style crowding, and calendar effects) to derive a composite win rate signal - Assign scores: 1 for large-cap signals, 0 for small-cap signals, and 0.5 for neutral signals. The average score represents the win rate[40][72] - **Kelly Formula for Allocation**: - Use the Kelly formula to calculate optimal allocation weights for large-cap and small-cap stocks based on odds and win rates - Formula: $ x = \frac{p*b - (1-p)}{b} $ where $ p $ is the win rate, $ b $ is the odds, and $ x $ is the allocation proportion[77] - Adjust weights to ensure they sum to 1 and avoid negative values, forming a complete rotation strategy[77][78] - **Model Evaluation**: The model effectively captures the rotation between large-cap and small-cap stocks, achieving significant excess returns and risk-adjusted performance[78] 2. Model Name: Weighted Size Rotation Strategy - **Model Construction Idea**: Adjust allocation weights between large-cap and small-cap stocks based on the difference in configuration scores derived from odds and win rates[82] - **Model Construction Process**: - Calculate the difference in configuration scores between large-cap and small-cap stocks - Standardize the score difference using a Z-score over the past 250 weeks - Map the standardized score to allocation weights using a predefined mapping table[83] - **Model Evaluation**: This strategy reduces maximum drawdown while maintaining a high level of excess returns and information ratio[84] 3. Model Name: Detailed Style Rotation Model - **Model Construction Idea**: Combine the size rotation model with a growth-value rotation model to form a detailed style rotation strategy, targeting large-cap growth, large-cap value, small-cap growth, and small-cap value[87] - **Model Construction Process**: - Use the size rotation model to determine the size preference (large-cap or small-cap) - Use the growth-value rotation model to determine the style preference (growth or value) - Combine the two signals to allocate to one of the four detailed styles[87] - **Model Evaluation**: The model demonstrates outstanding rotation effects, achieving the highest excess returns and information ratio among all strategies[90][92] --- Model Backtesting Results 1. Size Rotation Model Based on Odds and Win Rates - Total Return: 531.87% - Annualized Return: 23.70% - Annualized Volatility: 23.03% - Maximum Drawdown: 25.25% - Information Ratio (IR): 2.27 - Return-to-Drawdown Ratio: 2.79[79] 2. Weighted Size Rotation Strategy - Total Return: 204.13% - Annualized Return: 13.69% - Annualized Volatility: 22.02% - Maximum Drawdown: 29.17% - Information Ratio (IR): 2.47 - Return-to-Drawdown Ratio: 4.66[84] 3. Detailed Style Rotation Model - Total Return: 1329.51% - Annualized Return: 35.91% - Annualized Volatility: 23.97% - Maximum Drawdown: 23.37% - Information Ratio (IR): 3.11 - Return-to-Drawdown Ratio: 3.87[92] --- Quantitative Factors and Construction Methods 1. Factor Name: Shibor Signal - **Construction Idea**: Reflects the impact of liquidity conditions on small-cap and large-cap stocks[42] - **Construction Process**: - Calculate the historical percentile of the latest Shibor rate over the past year - Signal: If the percentile > 50%, favor large-cap; otherwise, favor small-cap[42] - **Backtesting Results**: - Annualized Excess Return: 11.46% - Information Ratio (IR): 1.23[43] 2. Factor Name: Short-Term Credit Spread - **Construction Idea**: Captures the impact of short-term credit market conditions on size rotation[47] - **Construction Process**: - Calculate the spread between 1-year and 7-day AAA+ short-term bond yields - Signal: If the 20-day average spread > 250-day average, favor large-cap; otherwise, favor small-cap[47] - **Backtesting Results**: - Annualized Excess Return: 7.41% - Information Ratio (IR): 0.79[48] 3. Factor Name: Market Trend - **Construction Idea**: Reflects the impact of market activity on size rotation[51] - **Construction Process**: - Compare the 5-day and 20-day moving averages of the CSI All Share Index - Signal: If the 5-day MA > 20-day MA and market volume is increasing, favor small-cap; otherwise, favor large-cap[51] - **Backtesting Results**: - Annualized Excess Return: 3.52% - Information Ratio (IR): 0.48[52] 4. Factor Name: Market Volatility - **Construction Idea**: Reflects the impact of market stability on size rotation[54] - **Construction Process**: - Compare the 20-day market volatility with its 3-year average - Signal: If volatility < average, favor large-cap; otherwise, favor small-cap[54] - **Backtesting Results**: - Annualized Excess Return: 13.18% - Information Ratio (IR): 1.42[55] 5. Factor Name: Style Momentum - **Construction Idea**: Captures the momentum effect in size rotation[57] - **Construction Process**: - Compare the past 4-week returns of CSI 300 and CSI 2000 indices - Signal: If CSI 300 return > CSI 2000 return, favor large-cap; otherwise, favor small-cap[57] - **Backtesting Results**: - Annualized Excess Return: 8.16% - Information Ratio (IR): 0.87[58] 6. Factor Name: Style Crowding - **Construction Idea**: Reflects the risk of style overcrowding and potential reversals[60] - **Construction Process**: - Calculate the historical percentile of the 20-day trading volume of the largest 20% and smallest 20% stocks - Signal: If large-cap volume > 75th percentile, favor small-cap; if small-cap volume > 75th percentile, favor large-cap[60] - **Backtesting Results**: - Annualized Excess Return: 6.63% - Information Ratio (IR): 0.93[61] 7. Factor Name: Calendar Effect - **Construction Idea**: Reflects the impact of periodic events on size rotation[63] - **Construction Process**: - Calculate the historical win rate of large-cap over small-cap for each calendar month - Signal: If the win rate > 50%, favor large-cap; otherwise, favor small-cap[66] - **Backtesting Results**: - Annualized Excess Return: 4.73% - Information Ratio (IR): 0.50[67] 8. Factor Name: Composite Win Rate Signal - **Construction Idea**: Combines all individual factors into a single composite signal[72] - **Construction Process**: - Average the scores of all individual factors to derive the composite win rate - Signal: If the composite score > 0.5, favor large-cap; otherwise, favor small-cap[72] - **Backtesting Results**: - Annualized Excess Return: 19.72% - Information Ratio (IR): 2.17[73]

- The "ERP and DRP standardized equal-weight calculation model" is used to compute A-share odds, which as of September end, declined to 0.2 standard deviations, indicating a neutral level[10] - The "macro victory rate scoring card model" synthesizes asset victory rates based on factors like credit and PMI pulses, which recently bottomed out, pushing A-share victory rates to 19%[10] - The "bond odds model" is constructed using the expected yield difference between long and short bonds, with recent bond odds retreating to -0.9 standard deviations, reflecting valuation risks for long bonds[11] - The "bond victory rate model" integrates credit and growth expansion data, showing a decline to -6%, indicating low victory rates[11] - The "AIAE indicator model" for US stocks is at 54%, its historical peak, corresponding to 2.4 standard deviations, signaling high pullback risks[15] - The "Federal Reserve liquidity index model" combines quantity and price dimensions, showing a tightening liquidity index at 20%, a medium-high level[15] Model Backtesting Results - ERP and DRP model: A-share odds at 0.2 standard deviations, victory rate at 19%[10] - Bond odds model: -0.9 standard deviations, victory rate at -6%[11] - AIAE indicator model: 54% historical peak, 2.4 standard deviations[15] - Federal Reserve liquidity index: 20% medium-high level[15] Factor Construction and Evaluation - Value factor: High odds (0.9 SD), medium trend (-0.3 SD), low crowding (-1.4 SD), comprehensive score 3, recommended for focus[19][22] - Small-cap factor: Medium odds (-0.2 SD), strong trend (1.6 SD), medium-low crowding (-0.5 SD), comprehensive score 2.2, configuration value improved[20][23] - Quality factor: High odds (1.4 SD), weak trend (-1.2 SD), medium-low crowding (-0.5 SD), comprehensive score 0.6, recommended for long-term attention[24][26] - Growth factor: Medium-high odds (0.8 SD), medium trend (0.1 SD), high crowding (1.0 SD), comprehensive score 0.1, recommended for standard allocation[27][28] Factor Backtesting Results - Value factor: Odds 0.9 SD, trend -0.3 SD, crowding -1.4 SD, score 3[19][22] - Small-cap factor: Odds -0.2 SD, trend 1.6 SD, crowding -0.5 SD, score 2.2[20][23] - Quality factor: Odds 1.4 SD, trend -1.2 SD, crowding -0.5 SD, score 0.6[24][26] - Growth factor: Odds 0.8 SD, trend 0.1 SD, crowding 1.0 SD, score 0.1[27][28] Strategy Construction and Evaluation - "Odds-enhanced strategy" allocates assets based on odds indicators under volatility constraints, achieving annualized returns of 6.6%-7.5% and maximum drawdowns of 2.4%-3.0% since 2011[39][41] - "Victory rate-enhanced strategy" uses macro victory rate scoring to allocate assets, achieving annualized returns of 6.3%-7.7% and maximum drawdowns of 2.3%-2.8% since 2011[42][44] - "Odds + victory rate strategy" combines risk budgets from both strategies, achieving annualized returns of 7.0%-7.6% and maximum drawdowns of 2.7%-2.8% since 2011[45][47] Strategy Backtesting Results - Odds-enhanced strategy: Annualized returns 6.6%-7.5%, max drawdowns 2.4%-3.0%[39][41] - Victory rate-enhanced strategy: Annualized returns 6.3%-7.7%, max drawdowns 2.3%-2.8%[42][44] - Odds + victory rate strategy: Annualized returns 7.0%-7.6%, max drawdowns 2.7%-2.8%[45][47]

Group 1 - The article discusses a macro quantitative framework that combines economic, liquidity, credit, and inflation factors for asset allocation and industry/style configuration [1][3] - The framework has been adjusted based on the changing mapping of macro variables to assets, with a focus on economic and liquidity indicators [1][5] - The performance of aggressive portfolios since 2013 shows an annualized return of approximately 8.5%, with a 0.6% excess return compared to the benchmark [3][5] Group 2 - The article highlights the impact of macroeconomic conditions on industry and style configurations, incorporating credit sensitivity into the analysis [5][7] - The macro-sensitive industry configuration has shown varying performance, with a notable decline since 2022, indicating the need for adjustments in selection criteria [7][10] - The article emphasizes the importance of market expectations in influencing macroeconomic indicators and their relationship with asset performance [13][18] Group 3 - The Factor Mimicking model is introduced to capture market expectations regarding macro variables, using a refined stock pool for better representation [19][20] - The construction of the Factor Mimicking portfolio aims to reflect the market's implicit views on economic, liquidity, inflation, and credit variables [19][23] - The article discusses the need for additional micro mappings to enhance the representation of macro variables, particularly in relation to corporate earnings and valuations [28][30] Group 4 - The article outlines the adjustments made to the macro variables based on market expectations, focusing on economic, liquidity, and credit dimensions [34][36] - The revised indicators are expected to improve asset allocation strategies, particularly in the context of equity markets [39][40] - The performance of the revised industry and style configurations indicates a positive impact from incorporating market expectations into the analysis [46][54]

Quantitative Models and Construction 1. Model Name: Odds + Win Rate Strategy - **Model Construction Idea**: This strategy combines the risk budget of the odds-based strategy and the win-rate-based strategy to create a comprehensive scoring system for asset allocation[3][48][54] - **Model Construction Process**: 1. The odds-based strategy allocates more to high-odds assets and less to low-odds assets under a target volatility constraint[48] 2. The win-rate-based strategy derives macro win-rate scores from five factors: monetary, credit, growth, inflation, and overseas, and allocates accordingly[51] 3. The combined strategy sums the risk budgets of the two strategies to form a unified allocation model[54] - **Model Evaluation**: The model demonstrates stable performance with low drawdowns and consistent returns over different time periods[54] 2. Model Name: Industry Rotation Strategy - **Model Construction Idea**: This strategy evaluates industries based on three dimensions: momentum/trend, turnover/volatility/beta (crowding), and IR (information ratio) over the past 12 months[43] - **Model Construction Process**: 1. Momentum and trend are measured using the IR of industries over the past 12 months[43] 2. Crowding is assessed using turnover ratio, volatility ratio, and beta ratio[43] 3. The strategy ranks industries based on these metrics and allocates to those with strong trends, low crowding, and high IR[43] - **Model Evaluation**: The strategy has shown strong excess returns and low tracking errors, making it a robust framework for industry allocation[43] --- Model Backtesting Results 1. Odds + Win Rate Strategy - **Annualized Return**: - 2011 onwards: 7.0% - 2014 onwards: 7.6% - 2019 onwards: 7.2%[54] - **Maximum Drawdown**: - 2011 onwards: 2.8% - 2014 onwards: 2.7% - 2019 onwards: 2.8%[54] - **Sharpe Ratio**: - 2011 onwards: 2.86 - 2014 onwards: 3.26 - 2019 onwards: 2.85[56] 2. Industry Rotation Strategy - **Excess Return**: - 2011 onwards: 13.1% - 2014 onwards: 13.0% - 2019 onwards: 10.8%[44] - **Tracking Error**: - 2011 onwards: 11.0% - 2014 onwards: 12.0% - 2019 onwards: 10.7%[44] - **IR**: - 2011 onwards: 1.18 - 2014 onwards: 1.08 - 2019 onwards: 1.02[44] --- Quantitative Factors and Construction 1. Factor Name: Value Factor - **Factor Construction Idea**: Measures stocks with strong trends, low crowding, and moderate odds[27] - **Factor Construction Process**: 1. Trend is measured at zero standard deviation[27] 2. Odds are at 0.3 standard deviation[27] 3. Crowding is at -1.3 standard deviation[27] - **Factor Evaluation**: The factor ranks highest among all style factors, making it a key focus for allocation[27] 2. Factor Name: Quality Factor - **Factor Construction Idea**: Focuses on high odds, weak trends, and low crowding, with potential for future trend confirmation[29] - **Factor Construction Process**: 1. Odds are at 1.7 standard deviation[29] 2. Trend is at -1.4 standard deviation[29] 3. Crowding is at -0.8 standard deviation[29] - **Factor Evaluation**: The factor shows left-side buy signals but requires trend confirmation for stronger allocation[29] 3. Factor Name: Growth Factor - **Factor Construction Idea**: Represents high odds, moderate trends, and moderate crowding, suitable for standard allocation[32] - **Factor Construction Process**: 1. Odds are at 0.9 standard deviation[32] 2. Trend is at -0.2 standard deviation[32] 3. Crowding is at 0.1 standard deviation[32] - **Factor Evaluation**: The factor is recommended for standard allocation due to its balanced characteristics[32] 4. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Characterized by low odds, strong trends, and high crowding, with high uncertainty[35] - **Factor Construction Process**: 1. Odds are at -0.7 standard deviation[35] 2. Trend is at 1.6 standard deviation[35] 3. Crowding is at 0.6 standard deviation[35] - **Factor Evaluation**: The factor is not recommended due to its high uncertainty and crowding[35] --- Factor Backtesting Results 1. Value Factor - **Odds**: 0.3 standard deviation - **Trend**: 0 standard deviation - **Crowding**: -1.3 standard deviation[27] 2. Quality Factor - **Odds**: 1.7 standard deviation - **Trend**: -1.4 standard deviation - **Crowding**: -0.8 standard deviation[29] 3. Growth Factor - **Odds**: 0.9 standard deviation - **Trend**: -0.2 standard deviation - **Crowding**: 0.1 standard deviation[32] 4. Small-Cap Factor - **Odds**: -0.7 standard deviation - **Trend**: 1.6 standard deviation - **Crowding**: 0.6 standard deviation[35]

Quantitative Models and Construction 1. Model Name: Odds Ratio + Win Rate Strategy - **Model Construction Idea**: This strategy combines the odds ratio and win rate metrics to allocate risk budgets across assets, aiming to optimize returns under historical data constraints [3][46] - **Model Construction Process**: - The odds ratio and win rate metrics are calculated for each asset based on historical data - The risk budgets derived from these two metrics are summed to form a composite score - Asset allocation is determined by the composite score, with higher scores receiving higher allocations - Current allocation recommendation: 11.5% equities, 2.2% gold, 86.3% bonds [3][46] - **Model Evaluation**: The model demonstrates stable performance with low drawdowns, making it suitable for risk-averse investors [3][46] 2. Model Name: Odds Ratio Enhanced Strategy - **Model Construction Idea**: Focuses on maximizing returns by overweighting high-odds assets and underweighting low-odds assets under a volatility constraint [40][41] - **Model Construction Process**: - Odds ratios are calculated for each asset - A fixed volatility constraint is applied to ensure risk control - Asset allocation is adjusted dynamically based on odds ratios - Current allocation recommendation: 15.6% equities, 2.9% gold, 81.5% bonds [40][41] - **Model Evaluation**: The strategy effectively balances risk and return, achieving consistent performance over time [40][41] 3. Model Name: Win Rate Enhanced Strategy - **Model Construction Idea**: Utilizes macroeconomic factors (e.g., monetary policy, credit, growth, inflation, and overseas conditions) to derive win rate scores for asset allocation [43][44] - **Model Construction Process**: - Win rate scores are calculated based on macroeconomic indicators - Asset allocation is determined by the win rate scores, favoring assets with higher scores - Current allocation recommendation: 6.6% equities, 1.7% gold, 91.7% bonds [43][44] - **Model Evaluation**: The strategy is robust in capturing macroeconomic trends, providing a defensive allocation approach [43][44] --- Model Backtesting Results 1. Odds Ratio + Win Rate Strategy - Annualized Return: 7.0% (2011–2025), 7.6% (2014–2025), 7.2% (2019–2025) - Maximum Drawdown: 2.8% (2011–2025), 2.7% (2014–2025), 2.8% (2019–2025) - Sharpe Ratio: 2.86 (2011–2025), 3.26 (2014–2025), 2.85 (2019–2025) [3][46][47] 2. Odds Ratio Enhanced Strategy - Annualized Return: 6.6% (2011–2025), 7.5% (2014–2025), 7.0% (2019–2025) - Maximum Drawdown: 3.0% (2011–2025), 2.4% (2014–2025), 2.4% (2019–2025) - Sharpe Ratio: 2.72 (2011–2025), 3.19 (2014–2025), 3.02 (2019–2025) [40][41][42] 3. Win Rate Enhanced Strategy - Annualized Return: 7.0% (2011–2025), 7.7% (2014–2025), 6.3% (2019–2025) - Maximum Drawdown: 2.8% (2011–2025), 2.3% (2014–2025), 2.3% (2019–2025) - Sharpe Ratio: 2.96 (2011–2025), 3.36 (2014–2025), 2.87 (2019–2025) [43][44][45] --- Quantitative Factors and Construction 1. Factor Name: Value Factor - **Factor Construction Idea**: Measures the relative attractiveness of value stocks based on odds, trends, and crowding metrics [18][20] - **Factor Construction Process**: - Odds: 0.2 standard deviations (higher indicates cheaper valuation) - Trend: -0.1 standard deviations (moderate level) - Crowding: -1.0 standard deviations (low crowding) - Composite Score: 1.0 (highest among all factors) [18][20] - **Factor Evaluation**: Strong trend and low crowding make it a top-performing factor [18][20] 2. Factor Name: Quality Factor - **Factor Construction Idea**: Focuses on high-quality stocks with favorable odds and low crowding, awaiting trend confirmation [20][21] - **Factor Construction Process**: - Odds: 1.4 standard deviations (high level) - Trend: -0.3 standard deviations (weak level) - Crowding: -0.8 standard deviations (low level) - Composite Score: 0.6 [20][21] - **Factor Evaluation**: Promising long-term potential but requires trend confirmation for stronger performance [20][21] 3. Factor Name: Growth Factor - **Factor Construction Idea**: Targets growth stocks with improving odds and moderate crowding [23][25] - **Factor Construction Process**: - Odds: 0.6 standard deviations (moderate level) - Trend: 0.02 standard deviations (neutral level) - Crowding: -0.1 standard deviations (moderate level) - Composite Score: 0.4 [23][25] - **Factor Evaluation**: Suitable for neutral allocation due to balanced metrics [23][25] 4. Factor Name: Small-Cap Factor - **Factor Construction Idea**: Captures small-cap stocks with strong trends but high crowding and low odds [26][28] - **Factor Construction Process**: - Odds: -0.5 standard deviations (low level) - Trend: 0.9 standard deviations (high level) - Crowding: 0.6 standard deviations (high level) - Composite Score: 0.0 [26][28] - **Factor Evaluation**: High uncertainty due to low odds and high crowding, requiring cautious approach [26][28] --- Factor Backtesting Results 1. Value Factor - Odds: 0.2 standard deviations - Trend: -0.1 standard deviations - Crowding: -1.0 standard deviations - Composite Score: 1.0 [18][20] 2. Quality Factor - Odds: 1.4 standard deviations - Trend: -0.3 standard deviations - Crowding: -0.8 standard deviations - Composite Score: 0.6 [20][21] 3. Growth Factor - Odds: 0.6 standard deviations - Trend: 0.02 standard deviations - Crowding: -0.1 standard deviations - Composite Score: 0.4 [23][25] 4. Small-Cap Factor - Odds: -0.5 standard deviations - Trend: 0.9 standard deviations - Crowding: 0.6 standard deviations - Composite Score: 0.0 [26][28]